Improving the Performance of Heterogeneous Data Centers through Redundancy

Anton, Elene; Ayesta, Urtzi; Jonckheere, Matthieu; Verloop, Ina Maria

doi:10.1145/3428333

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…It is worth emphasizing that this assumption is satisfied in a wide range of systems and non-CRP scenarios, including but certainly not restricted to, so-called nested systems as also studied in [4,16,17]. Indeed, the compatibility constraints of the system in Example 4 do not yield a nested system (Figure 2a), yet the associated DAG satisfies Assumption 1 (Figure 2b).…”

Section: Resultsmentioning

confidence: 98%

Job assignment in large-scale service systems with affinity relations

2019

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We consider load balancing in service systems with affinity relations between jobs and servers. Specifically, an arriving job can be allocated to a fast, primary server from a particular selection associated with this job or to a secondary server to be processed at a slower rate. Such job-server affinity relations can model network topologies based on geographical proximity, or data locality in cloud scenarios. We introduce load balancing schemes that allocate jobs to primary servers if available, and otherwise to secondary servers. A novel coupling construction is developed to obtain stability conditions and performance bounds using a coupling technique. We also conduct a fluid limit analysis for symmetric model instances, which reveals a delicate interplay between the model parameters and load balancing performance.

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Section: Resultsmentioning

confidence: 98%

Job assignment in large-scale service systems with affinity relations

2019

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“…Kwon and Gautam [28] and Cho and Ko [29] investigated methods to time stabilize the performance of stochastic service systems that well-model cloud data centers. Anton et al [30] first showed that a redundancy system (e.g., MapReduce) could help improve the performance of data center computing in case the servers' capacities are sufficiently heterogeneous. Recently, Harchol-Balter [1] published a seminal paper that examines the open problems in queueing theory inspired by data center computing industry.…”

Section: B Real-time Operationmentioning

confidence: 99%

Power- and QoS-Aware Job Assignment With Dynamic Speed Scaling for Cloud Data Center Computing

Cho

2022

IEEE Access

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As the usage of mission-critical mobile applications increases in Industry 4.0, such as smart manufacturing and self-driving cars, the cloud computing paradigm and its supporting data centers have become more crucial. However, a common practice in the cloud data center computing industry tends to supply a surfeit of computing resources mainly for a robust quality-of-service (QoS). In this paper, we propose a simple real-time algorithm which combines a power-aware job assignment policy for a centralized job dispatcher and a power-and QoS-aware dynamic speed scaling policy for each physical machine (PM). The job assignment policy is called "Join the Least Power Consuming (LPC) Server" that routes an incoming cloud job to a server spending minimum power upon request. The server-side adaptive speed scaling policy expedites energy efficiency and satisfies response time-associated QoS condition. We call this policy "Minimizing Earliness (ME)" since it manages the server speed towards finishing jobs at their deadlines as precisely as possible, reducing the earliness of job completion. The design principle of LPC-ME combination supports both energy efficiency and service quality required in cloud data centers. Numerical experiments compare the proposed algorithm's power consumption and response time with those of existing popular policies and demonstrate better energy efficiency with negligible degradation of service quality.INDEX TERMS Cloud computing, data centers, energy efficiency, queueing analysis, resource management.

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“…Detailed proof for this result is omitted because of space constraints. This approach is exactly the implementation of (10).…”

Section: Extending To Non-fractional Workloadsmentioning

confidence: 99%

Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs

Hong-xia¹,

Deng²,

Yin³

et al. 2021

Preprint

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Serverless computing is leading the way to a simplified and general purpose programming model for the cloud. A key enabler behind serverless is efficient load balancing, which routes continuous workloads to appropriate backend resources. However, current load balancing algorithms implemented in Kubernetes native serverless platforms are simple heuristics without performance guarantee. Although policies such as Pod or JFIQ yield asymptotically optimal mean response time, the information they depend on are usually unavailable. In addition, dispatching jobs with strict deadlines, fractional workloads, and maximum parallelism bound to limited resources online is difficult because the resource allocation decisions for jobs are intertwined. To design an online load balancing algorithm without assumptions on distributions while maximizing the social welfare, we construct several pseudo-social welfare functions and cost functions, where the latter is to estimate the marginal cost for provisioning services to every newly arrived job based on present resource surplus. The proposed algorithm, named OnSocMax, works by following the solutions of several convex pseudo-social welfare maximization problems. It is proved to be α-competitive for some α at least 2. We also validate OnSocMax with simulations and the results show that it distinctly outperforms several handcrafted benchmarks.

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Improving the Performance of Heterogeneous Data Centers through Redundancy

Cited by 12 publications

References 22 publications

Job assignment in large-scale service systems with affinity relations

Job assignment in large-scale service systems with affinity relations

Power- and QoS-Aware Job Assignment With Dynamic Speed Scaling for Cloud Data Center Computing

Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs

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